From 12933cfd6033f706b525b41fe9cf4fe400e702aa Mon Sep 17 00:00:00 2001 From: mrq Date: Fri, 17 Feb 2023 06:01:14 +0000 Subject: [PATCH] added dropdown to select which whisper model to use for transcription, added note that FFMPEG is required --- README.md | 16 +++++++++++++++- src/utils.py | 8 ++++++-- src/webui.py | 9 +++++++-- start.bat | 1 + 4 files changed, 29 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 2983fb8..45a7e76 100755 --- a/README.md +++ b/README.md @@ -223,7 +223,17 @@ To import a voice, click `Import Voice`. Remember to click `Refresh Voice List` This tab will contain a collection of sub-tabs pertaining to training. -#### Configuration +#### Prepare Dataset + +This section will aid in preparing the dataset for fine-tuning. + +With it, you simply select a voice, then click the button, and wait for the console to tell you it's done. The results will be saved to `./training/{voice name}/`. + +The web UI will leverage [openai/whisper](https://github.com/openai/whisper) to transcribe a given sample source, and split them into convenient pieces. + +**!**NOTE**!**: transcription leverages FFMPEG, so please make sure you either have an FFMPEG installed visible to your PATH, or drop the binary in the `./bin/` folder. + +#### Generate Configuration This will generate the YAML necessary to feed into training. For now, you can set: * `Batch Size`: size of batches for training, more batches = faster training, at the cost of higher VRAM. setting this to 1 will lead to problems @@ -255,6 +265,10 @@ Below are settings that override the default launch arguments. Some of these req * `Embed Output Metadata`: enables embedding the settings and latents used to generate that audio clip inside that audio clip. Metadata is stored as a JSON string in the `lyrics` tag. * `Slimmer Computed Latents`: falls back to the original, 12.9KiB way of storing latents (without the extra bits required for using the CVVP model). * `Voice Fixer`: runs each generated audio clip through `voicefixer`, if available and installed. +* `Use CUDA for Voice Fixer`: allows voicefixer to use CUDA. Speeds up cleaning the output, but at the cost of more VRAM consumed. Disable if you OOM. +* `Device Override`: overrides the device name used to pass to PyTorch for hardware acceleration. You can use the accompanied `list_devices.py` script to map valid strings to GPU names. You can also pass `cpu` if you want to fallback to software mode. +* `Whisper Model`: the specific model to use for Whisper transcription, when preparing a dataset to finetune with. + * `Voice Latent Max Chunk Size`: during the voice latents calculation pass, this limits how large, in bytes, a chunk can be. Large values can run into VRAM OOM errors. * `Sample Batch Size`: sets the batch size when generating autoregressive samples. Bigger batches result in faster compute, at the cost of increased VRAM consumption. Leave to 0 to calculate a "best" fit. * `Concurrency Count`: how many Gradio events the queue can process at once. Leave this over 1 if you want to modify settings in the UI that updates other settings while generating audio clips. diff --git a/src/utils.py b/src/utils.py index 4036fc6..169959f 100755 --- a/src/utils.py +++ b/src/utils.py @@ -57,6 +57,7 @@ def setup_args(): 'voice-fixer-use-cuda': True, 'force-cpu-for-conditioning-latents': False, 'device-override': None, + 'whisper-model': "base", 'concurrency-count': 2, 'output-sample-rate': 44100, 'output-volume': 1, @@ -80,6 +81,7 @@ def setup_args(): parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.") parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)") parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch") + parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.") parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass") parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once") parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)") @@ -463,7 +465,7 @@ whisper_model = None def prepare_dataset( files, outdir ): global whisper_model if whisper_model is None: - whisper_model = whisper.load_model("base") + whisper_model = whisper.load_model(args.whisper_model) os.makedirs(outdir, exist_ok=True) @@ -653,7 +655,7 @@ def get_voice_list(dir=get_voice_dir()): os.makedirs(dir, exist_ok=True) return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"] -def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ): +def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, device_override, whisper_model, sample_batch_size, concurrency_count, output_sample_rate, output_volume ): global args args.listen = listen @@ -663,6 +665,7 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on args.low_vram = low_vram args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents args.device_override = device_override + args.whisper_model = whisper_model args.sample_batch_size = sample_batch_size args.embed_output_metadata = embed_output_metadata args.latents_lean_and_mean = latents_lean_and_mean @@ -680,6 +683,7 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on 'models-from-local-only':args.models_from_local_only, 'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents, 'device-override': args.device_override, + 'whisper-model': args.whisper_model, 'sample-batch-size': args.sample_batch_size, 'embed-output-metadata': args.embed_output_metadata, 'latents-lean-and-mean': args.latents_lean_and_mean, diff --git a/src/webui.py b/src/webui.py index 253dd4c..976b292 100755 --- a/src/webui.py +++ b/src/webui.py @@ -370,6 +370,7 @@ def setup_gradio(): ] ) with gr.Tab("Training"): + with gr.Tab("Prepare Dataset"): with gr.Row(): with gr.Column(): dataset_settings = [ @@ -377,6 +378,7 @@ def setup_gradio(): ] dataset_voices = dataset_settings[0] + with gr.Column(): prepare_dataset_button = gr.Button(value="Prepare") def prepare_dataset_proxy( voice ): @@ -387,7 +389,8 @@ def setup_gradio(): inputs=dataset_settings, outputs=None ) - + with gr.Tab("Generate Configuration"): + with gr.Row(): with gr.Column(): training_settings = [ gr.Slider(label="Batch Size", value=128), @@ -395,6 +398,7 @@ def setup_gradio(): gr.Number(label="Print Frequency", value=50), gr.Number(label="Save Frequency", value=50), ] + save_yaml_button = gr.Button(value="Save Training Configuration") with gr.Column(): training_settings = training_settings + [ gr.Textbox(label="Training Name", placeholder="finetune"), @@ -403,7 +407,7 @@ def setup_gradio(): gr.Textbox(label="Validation Name", placeholder="finetune"), gr.Textbox(label="Validation Path", placeholder="./experiments/finetune/val.txt"), ] - save_yaml_button = gr.Button(value="Save Training Configuration") + save_yaml_button.click(save_training_settings, inputs=training_settings, outputs=None @@ -424,6 +428,7 @@ def setup_gradio(): gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda), gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents), gr.Textbox(label="Device Override", value=args.device_override), + gr.Dropdown(label="Whisper Model", value=args.whisper_model, choices=["tiny", "base", "small", "medium", "large"]), ] gr.Button(value="Check for Updates").click(check_for_updates) gr.Button(value="Reload TTS").click(reload_tts) diff --git a/start.bat b/start.bat index 14c8546..61ff22f 100755 --- a/start.bat +++ b/start.bat @@ -1,4 +1,5 @@ call .\venv\Scripts\activate.bat +set PATH=.\bin\;%PATH% python .\src\main.py deactivate pause \ No newline at end of file